Human pose tracking using online latent structured support vector machine

Tracking human poses in a video is a challenging problem and has numerous applications. The task is particularly difficult in realistic scenes because of several intrinsic and extrinsic factors, including complicated and fast movements, occlusions and lighting changes. We propose an online learning approach for tracking human poses using latent structured Support Vector Machine (SVM). The first frame in a video is used for training, in which body parts are initialized by users and tracking models are learned using latent structured SVM. The models are updated for each subsequent frame in the video sequence. To solve the occlusion problem, we formulate a Prize-Collecting Steiner tree (PCST) problem and use a branch-and-cut algorithm to refine the detection of body parts. Experiments using several challenging videos demonstrate that the proposed method outperforms two state-of-the-art methods.